A Comprehensive Data Schema for Digital Twin Construction

J. Schlenger, T. Yeung, S. Vilgertshofer, J. Martinez, R. Sacks, A. Borrmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Lean construction, originating from lean management, aims to proactively improve the overall efficiency of construction processes. This requires continuous assessment of performance to identify key problems, which allows continuous improvement of construction processes. Novel digital twin approaches form an excellent technological foundation for performance assessment through regular updates with product and process information directly from the construction site. While some publications favour a schema-less approach to digital twins, we argue that well-defined data structures are required to represent complex information reliably and transparently. Existing process and product models are inadequate with respect to the requirements of a digital twin of the construction phase. As a result, we introduce a new process-oriented model that provides an improved basis for advanced process evaluation in the digital twin environment. This data schema, which is the main outcome of this paper, is presented in UML format together with a first approach to transfer it to an ontology usable in the Semantic Web context.

Original languageEnglish
Title of host publicationProceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering
EditorsJochen Teizer, Carl Peter Leslie Schultz
Pages34-44
Number of pages11
ISBN (Electronic)9788775075218
DOIs
StatePublished - 2022
Event29th International Workshop on Intelligent Computing in Engineering, EG-ICE 2022 - Aarhus, Denmark
Duration: 6 Jul 20228 Jul 2022

Publication series

NameProceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

Conference

Conference29th International Workshop on Intelligent Computing in Engineering, EG-ICE 2022
Country/TerritoryDenmark
CityAarhus
Period6/07/228/07/22

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications

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